An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions

Abstract EN

SNP-SNP interactions have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases.

Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing.

In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions.

Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning, dynamic inertia weight, and a postprocedure.

Opposition-based learning not only enhances the global explorative ability, but also avoids premature convergence.

Dynamic inertia weight allows particles to cover a wider search space when the considered SNP is likely to be a random one and converges on promising regions of the search space while capturing a highly suspected SNP.

The postprocedure is used to carry out a deep search in highly suspected SNP sets.

Experiments of IOBLPSO are performed on both simulation data sets and a real data set of age-related macular degeneration, results of which demonstrate that IOBLPSO is promising in detecting SNP-SNP interactions.

IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions.